{
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   "cell_type": "markdown",
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   "source": [
    "# Simulating readout noise on the Rigetti Quantum Virtual Machine\n",
    "© Copyright 2018, Rigetti Computing. \n",
    "$$\n",
    "\\newcommand{ket}[1]{\\left|{#1}\\right\\rangle}\n",
    "\\newcommand{bra}[1]{\\left\\langle {#1}\\right|}\n",
    "\\newcommand{tr}[1]{\\mathrm{Tr}\\,\\left[ {#1}\\right]}\n",
    "\\newcommand{expect}[1]{\\left\\langle {#1} \\right \\rangle}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Theoretical Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Qubit-Readout can be corrupted in a variety of ways. The two most relevant error mechanisms on the Rigetti QPU right now are:\n",
    "\n",
    "1. Transmission line noise that makes a 0-state look like a 1-state or vice versa. We call this **classical readout bit-flip error**. This type of readout noise can be reduced by tailoring optimal readout pulses and using superconducting, quantum limited amplifiers to amplify the readout signal before it is corrupted by classical noise at the higher temperature stages of our cryostats.\n",
    "2. T1 qubit decay during readout (our readout operations can take more than a µsecond unless they have been specially optimized), which leads to readout signals that initially behave like 1-states but then collapse to something resembling a 0-state. We will call this **T1-readout error**. This type of readout error can be reduced by achieving shorter readout pulses relative to the T1 time, i.e., one can try to reduce the readout pulse length, or increase the T1 time or both."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Qubit measurements\n",
    "\n",
    "This section provides the necessary theoretical foundation for accurately modeling noisy quantum measurements on superconducting quantum processors. It relies on some of the abstractions (density matrices, Kraus maps) introduced in our notebook on [gate noise models](GateNoiseModels.ipynb).\n",
    "\n",
    "The most general type of measurement performed on a single qubit at a single time can be characterized by some set $\\mathcal{O}$ of measurement outcomes, e.g., in the simplest case $\\mathcal{O} = \\{0, 1\\}$, and some unnormalized quantum channels (see notebook on gate noise models) that encapsulate\n",
    "1. the probability of that outcome\n",
    "2. how the qubit state is affected conditional on the measurement outcome.\n",
    "\n",
    "Here the _outcome_ is understood as classical information that has been extracted from the quantum system.\n",
    "\n",
    "### Projective, ideal measurement\n",
    "The simplest case that is usually taught in introductory quantum mechanics and quantum information courses are Born's rule and the projection postulate which state that there exist a complete set of orthogonal projection operators \n",
    "$$\n",
    "P_{\\mathcal{O}} := \\{\\Pi_x \\text{ Projector }\\mid x \\in \\mathcal{O}\\},\n",
    "$$\n",
    "i.e., one for each measurement outcome. Any projection operator must satisfy $\\Pi_x^\\dagger = \\Pi_x = \\Pi_x^2$ and for an _orthogonal_ set of projectors any two members satisfy \n",
    "$$\n",
    "\\Pi_x\\Pi_y = \\delta_{xy} \\Pi_x = \\begin{cases} 0 & \\text{ if } x \\ne y \\\\ \\Pi_x & \\text{ if } x = y \\end{cases}\n",
    "$$\n",
    "and for a _complete_ set we additionally demand that $\\sum_{x\\in\\mathcal{O}} \\Pi_x = 1$.\n",
    "Following our introduction to gate noise, we write quantum states as density matrices as this is more general and in closer correspondence with classical probability theory.\n",
    "\n",
    "With these the probability of outcome $x$ is given by $p(x) = \\tr{\\Pi_x \\rho \\Pi_x} = \\tr{\\Pi_x^2 \\rho} = \\tr{\\Pi_x \\rho}$ and the post measurement state is\n",
    "$$\n",
    "\\rho_x = \\frac{1}{p(x)} \\Pi_x \\rho \\Pi_x,\n",
    "$$\n",
    "which is the projection postulate applied to mixed states.\n",
    "\n",
    "If we were a sloppy quantum programmer and accidentally erased the measurement outcome then our best guess for the post measurement state would be given by something that looks an awful lot like a Kraus map:\n",
    "$$\n",
    "\\rho_{\\text{post measurement}} = \\sum_{x\\in\\mathcal{O}} p(x) \\rho_x = \\sum_{x\\in\\mathcal{O}} \\Pi_x \\rho \\Pi_x.\n",
    "$$\n",
    "The completeness of the projector set ensures that the trace of the post measurement is still 1 and the Kraus map form of this expression ensures that $\\rho_{\\text{post measurement}}$ is a positive (semi-)definite operator.\n",
    "\n",
    "### Classical readout bit-flip error\n",
    "\n",
    "Consider now the ideal measurement as above, but where the outcome $x$ is transmitted across a noisy classical channel that produces a final outcome $x'\\in \\mathcal{O}' = \\{0', 1'\\}$ according to some conditional probabilities $p(x'|x)$ that can be recorded in the _assignment probability matrix_\n",
    "$$\n",
    "P_{x'|x} = \\begin{pmatrix} \n",
    "p(0 | 0) & p(0 | 1) \\\\\n",
    "p(1 | 0) & p(1 | 1)\n",
    "\\end{pmatrix}\n",
    "$$\n",
    "Note that this matrix has only two independent parameters as each column must be a valid probability distribution, i.e. all elements are non-negative and each column sums to 1.\n",
    "\n",
    "This matrix allows us to obtain the probabilities $\\mathbf{p}' := (p(x'=0), p(x'=1))^T$ from the original outcome probabilities $\\mathbf{p} := (p(x=0), p(x=1))^T$  via $\\mathbf{p}' = P_{x'|x}\\mathbf{p}$.\n",
    "The difference relative to the ideal case above is that now an outcome $x' = 0$ does not necessarily imply that the post measurement state is truly $\\Pi_{0} \\rho \\Pi_{0} / p(x=0)$. Instead, the post measurement state given a noisy outcome $x'$ must be\n",
    "\\begin{align}\n",
    "\\rho_{x'} & = \\sum_{x\\in \\mathcal{O}} p(x|x') \\rho_x \\\\\n",
    "          & = \\sum_{x\\in \\mathcal{O}} p(x'|x)\\frac{p(x)}{p(x')} \\rho_x \\\\\n",
    "          & = \\frac{1}{p(x')}\\sum_{x\\in \\mathcal{O}} p(x'|x) \\Pi_x \\rho \\Pi_x\n",
    "\\end{align}\n",
    "where \n",
    "\\begin{align}\n",
    "p(x') & = \\sum_{x\\in\\mathcal{O}} p(x'|x) p(x)  \\\\\n",
    "& = \\tr{\\sum_{x\\in \\mathcal{O}} p(x'|x) \\Pi_x \\rho \\Pi_x} \\\\\n",
    "& = \\tr{\\rho \\sum_{x\\in \\mathcal{O}} p(x'|x)\\Pi_x} \\\\\n",
    "& = \\tr{\\rho E_x'}.\n",
    "\\end{align}\n",
    "where we have exploited the cyclical property of the trace $\\tr{ABC}=\\tr{BCA}$ and the projection property $\\Pi_x^2 = \\Pi_x$. This has allowed us to derive the noisy outcome probabilities from a set of positive operators\n",
    "$$\n",
    "E_{x'} := \\sum_{x\\in \\mathcal{O}} p(x'|x)\\Pi_x \\ge 0\n",
    "$$\n",
    "that must sum to 1: \n",
    "$$\n",
    "\\sum_{x'\\in\\mathcal{O}'} E_x' = \\sum_{x\\in\\mathcal{O}}\\underbrace{\\left[\\sum_{x'\\in\\mathcal{O}'} p(x'|x)\\right]}_{1}\\Pi_x = \\sum_{x\\in\\mathcal{O}}\\Pi_x = 1.\n",
    "$$\n",
    "The above result is a type of generalized **Bayes' theorem** that is extremely useful for this type of (slightly) generalized measurement and the family of operators $\\{E_{x'}| x' \\in \\mathcal{O}'\\}$ whose expectations give the probabilities is called a **positive operator valued measure** (POVM). These operators are not generally orthogonal nor valid projection operators but they naturally arise in this scenario. This is not yet the most general type of measurement, but it will get us pretty far."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to model $T_1$ error\n",
    "T1 type errors fall outside our framework so far as they involve a scenario in which the _quantum state itself_ is corrupted during the measurement process in a way that potentially erases the pre-measurement information as opposed to a loss of purely classical information. The most appropriate framework for describing this is given by that of measurement instruments, but for the practical purpose of arriving at a relatively simple description, we propose describing this by a T1 damping Kraus map followed by the noisy readout process as described above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further reading\n",
    "Chapter 3 of John Preskill's lecture notes http://www.theory.caltech.edu/people/preskill/ph229/notes/chap3.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How do I get started?\n",
    "\n",
    "1. Come up with a good guess for your readout noise parameters $p(0|0)$ and $p(1|1)$, the off-diagonals then follow from the normalization of $P_{x'|x}$. If your assignment fidelity $F$ is given, and you assume that the classical bit flip noise is roughly symmetric, then a good approximation is to set $p(0|0)=p(1|1)=F$.\n",
    "2. For your QUIL program `p`, and a qubit index `q` call:\n",
    "    ```\n",
    "    p.define_noisy_readout(q, p00, p11)\n",
    "    ```\n",
    "    where you should replace `p00` and `p11` with the assumed probabilities.\n",
    "\n",
    "### Estimate $P_{x'|x}$ yourself!\n",
    "You can also run some simple experiments to estimate the assignment probability matrix directly from a QPU.\n",
    "\n",
    "**Scroll down for some examples!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from pyquil.quil import Program, MEASURE, Pragma\n",
    "from pyquil.api.qvm import QVMConnection\n",
    "from pyquil.gates import I, X, RX, H, CNOT\n",
    "from pyquil.noise import (estimate_bitstring_probs, correct_bitstring_probs, \n",
    "                          bitstring_probs_to_z_moments, estimate_assignment_probs)\n",
    "\n",
    "DARK_TEAL = '#48737F'\n",
    "FUSCHIA = '#D6619E'\n",
    "BEIGE = '#EAE8C6'\n",
    "\n",
    "cxn = QVMConnection()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1: Rabi sequence with noisy readout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.2 s, sys: 73.6 ms, total: 1.27 s\n",
      "Wall time: 3.97 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# number of angles\n",
    "num_theta = 101\n",
    "\n",
    "# number of program executions\n",
    "trials = 200\n",
    "\n",
    "thetas = np.linspace(0, 2*np.pi, num_theta)\n",
    "\n",
    "p00s = [1., 0.95, 0.9, 0.8]\n",
    "\n",
    "results_rabi = np.zeros((num_theta, len(p00s)))\n",
    "\n",
    "for jj, theta in enumerate(thetas):\n",
    "    for kk, p00 in enumerate(p00s):\n",
    "        cxn.random_seed = hash((jj, kk))\n",
    "        p = Program(RX(theta, 0))\n",
    "        # assume symmetric noise p11 = p00\n",
    "        p.define_noisy_readout(0, p00=p00, p11=p00)\n",
    "        p.measure(0, 0)\n",
    "        res = cxn.run(p, [0], trials=trials)\n",
    "        results_rabi[jj, kk] = np.sum(res)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x104314250>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1042dd3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "for jj, (p00, c) in enumerate(zip(p00s, [DARK_TEAL, FUSCHIA, \"k\", \"gray\"])):\n",
    "    plt.plot(thetas, results_rabi[:, jj]/trials, c=c, label=r\"$p(0|0)=p(1|1)={:g}$\".format(p00))\n",
    "plt.legend(loc=\"best\")\n",
    "plt.xlim(*thetas[[0,-1]])\n",
    "plt.ylim(-.1, 1.1)\n",
    "plt.grid(alpha=.5)\n",
    "plt.xlabel(r\"RX angle $\\theta$ [radian]\", size=16)\n",
    "plt.ylabel(r\"Excited state fraction $n_1/n_{\\rm trials}$\", size=16)\n",
    "plt.title(\"Effect of classical readout noise on Rabi contrast.\", size=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2: Estimate the assignment probabilities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimate assignment probabilities for a perfect quantum computer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.],\n",
       "       [ 0.,  1.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimate_assignment_probs(0, 1000, cxn, Program())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Re-Estimate assignment probabilities for an imperfect quantum computer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cxn.seed = None\n",
    "header0 = Program().define_noisy_readout(0, .85, .95)\n",
    "header1 = Program().define_noisy_readout(1, .8, .9)\n",
    "header2 = Program().define_noisy_readout(2, .9, .85)\n",
    "\n",
    "ap0 = estimate_assignment_probs(0, 100000, cxn, header0)\n",
    "ap1 = estimate_assignment_probs(1, 100000, cxn, header1)\n",
    "ap2 = estimate_assignment_probs(2, 100000, cxn, header2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.84967  0.04941]\n",
      " [ 0.15033  0.95059]]\n",
      "[[ 0.80058  0.09993]\n",
      " [ 0.19942  0.90007]]\n",
      "[[ 0.90048  0.14988]\n",
      " [ 0.09952  0.85012]]\n"
     ]
    }
   ],
   "source": [
    "print(ap0, ap1, ap2, sep=\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 3: Use `pyquil.noise.correct_bitstring_probs` to correct for noisy readout\n",
    "\n",
    "### 3a) Correcting the Rabi signal from above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ap_last = np.array([[p00s[-1], 1 - p00s[-1]], \n",
    "                    [1 - p00s[-1], p00s[-1]]])\n",
    "corrected_last_result = [correct_bitstring_probs([1-p, p], [ap_last])[1] for p in results_rabi[:, -1] / trials]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1055e7310>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105662a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "for jj, (p00, c) in enumerate(zip(p00s, [DARK_TEAL, FUSCHIA, \"k\", \"gray\"])):\n",
    "    if jj not in [0, 3]:\n",
    "        continue\n",
    "    plt.plot(thetas, results_rabi[:, jj]/trials, c=c, label=r\"$p(0|0)=p(1|1)={:g}$\".format(p00), alpha=.3)\n",
    "plt.plot(thetas, corrected_last_result, c=\"red\", label=r\"Corrected $p(0|0)=p(1|1)={:g}$\".format(p00s[-1]))\n",
    "plt.legend(loc=\"best\")\n",
    "plt.xlim(*thetas[[0,-1]])\n",
    "plt.ylim(-.1, 1.1)\n",
    "plt.grid(alpha=.5)\n",
    "plt.xlabel(r\"RX angle $\\theta$ [radian]\", size=16)\n",
    "plt.ylabel(r\"Excited state fraction $n_1/n_{\\rm trials}$\", size=16)\n",
    "plt.title(\"Corrected contrast\", size=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We find that the corrected signal is fairly noisy (and sometimes exceeds the allowed interval $[0,1]$) due to the overall very small number of samples $n=200$.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3b) In this example we will create a GHZ state $\\frac{1}{\\sqrt{2}}\\left[\\left|000\\right\\rangle + \\left|111\\right\\rangle \\right]$ and measure its outcome probabilities with and without the above noise model. We will then see how the Pauli-Z moments that indicate the qubit correlations are corrupted (and corrected) using our API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H 0\n",
      "CNOT 0 1\n",
      "CNOT 1 2\n",
      "MEASURE 0 [0]\n",
      "MEASURE 1 [1]\n",
      "MEASURE 2 [2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ghz_prog = Program(H(0), CNOT(0, 1), CNOT(1, 2),\n",
    "                   MEASURE(0, 0), MEASURE(1, 1), MEASURE(2, 2))\n",
    "print(ghz_prog)\n",
    "results = cxn.run(ghz_prog, [0, 1, 2], trials=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PRAGMA READOUT-POVM 0 \"(0.85 0.050000000000000044 0.15000000000000002 0.95)\"\n",
      "PRAGMA READOUT-POVM 1 \"(0.8 0.09999999999999998 0.19999999999999996 0.9)\"\n",
      "PRAGMA READOUT-POVM 2 \"(0.9 0.15000000000000002 0.09999999999999998 0.85)\"\n",
      "H 0\n",
      "CNOT 0 1\n",
      "CNOT 1 2\n",
      "MEASURE 0 [0]\n",
      "MEASURE 1 [1]\n",
      "MEASURE 2 [2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "header = header0 + header1 + header2\n",
    "noisy_ghz = header + ghz_prog\n",
    "print(noisy_ghz)\n",
    "noisy_results = cxn.run(noisy_ghz, [0, 1, 2], trials=10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uncorrupted probability for $\\left|000\\right\\rangle$ and $\\left|111\\right\\rangle$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.50419999999999998, 0.49580000000000002)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs = estimate_bitstring_probs(results)\n",
    "probs[0, 0, 0], probs[1, 1, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected the outcomes `000` and `111` each have roughly probability $1/2$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Corrupted probability for $\\left|011\\right\\rangle$ and $\\left|100\\right\\rangle$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.30869999999999997, 0.3644)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noisy_probs = estimate_bitstring_probs(noisy_results)\n",
    "noisy_probs[0, 0, 0], noisy_probs[1, 1, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The noise-corrupted outcome probabilities deviate significantly from their ideal values!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Corrected probability for $\\left|011\\right\\rangle$ and $\\left|100\\right\\rangle$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.50397601453064977, 0.49866843912900716)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrected_probs = correct_bitstring_probs(noisy_probs, [ap0, ap1, ap2])\n",
    "corrected_probs[0, 0, 0], corrected_probs[1, 1, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The corrected outcome probabilities are much closer to the ideal value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimate $\\langle Z_0^{j} Z_1^{k} Z_2^{\\ell}\\rangle$ for $jkl=100, 010, 001$ from non-noisy data\n",
    "*We expect these to all be very small*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0083999999999999631, 0.0083999999999999631, 0.0083999999999999631)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zmoments = bitstring_probs_to_z_moments(probs)\n",
    "zmoments[1, 0, 0], zmoments[0, 1, 0], zmoments[0, 0, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimate $\\langle Z_0^{j} Z_1^{k} Z_2^{\\ell}\\rangle$ for $jkl=110, 011, 101$ from non-noisy data\n",
    "*We expect these to all be close to 1.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 1.0, 1.0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zmoments[1, 1, 0], zmoments[0, 1, 1], zmoments[1, 0, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimate $\\langle Z_0^{j} Z_1^{k} Z_2^{\\ell}\\rangle$ for $jkl=100, 010, 001$ from noise-corrected data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0071476770049732075, -0.0078641261685578612, 0.0088462563282706852)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zmoments_corr = bitstring_probs_to_z_moments(corrected_probs)\n",
    "zmoments_corr[1, 0, 0], zmoments_corr[0, 1, 0], zmoments_corr[0, 0, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimate $\\langle Z_0^{j} Z_1^{k} Z_2^{\\ell}\\rangle$ for $jkl=110, 011, 101$ from noise-corrected data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.99477496902638118, 1.0008376440216553, 1.0149652015905912)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zmoments_corr[1, 1, 0], zmoments_corr[0, 1, 1], zmoments_corr[1, 0, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Overall the correction can restore the contrast in our multi-qubit observables, though we also see that the correction can lead to slightly non-physical expectations. This effect is reduced the more samples we take."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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